Streams using persistent tables

ABSTRACT

A system or persistent table may be generated storing changelog information of a primary base table. The system table may then be used to create streams of relevant information. In some examples, the streams may read from the system table for information past a retention period of the primary table while reading from the primary table information in the retention period.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 63/143,184 filed Jan. 29, 2021, the contentsof which are incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to a network-based databasesystem and, more specifically, to generating change data capturedeterminations, such as streams or virtual tables, using persistenttables (also referred to as system tables).

BACKGROUND

Network-based database systems may be provided through a cloud platform,which allows organizations and users to store, manage, and retrieve datafrom the cloud. With respect to type of data processing, a databasesystem could implement online transactional processing, onlineanalytical processing, a combination of the two, and/or another type ofdata processing. Moreover, a database platform could be or include arelational database management system and/or one or more other types ofdatabase management systems.

One such example is a cloud data warehouse (also referred to as a“network-based data warehouse” or simply as a “data warehouse”), whichis a network-based system used for data analysis and reporting thatcomprises a central repository of integrated data from one or moredisparate sources. A cloud data warehouse can store current andhistorical data that can be used for creating analytical reports for anenterprise. To this end, data warehouses can provide businessintelligence tools, tools to extract, transform, and load data into therepository, and tools to manage and retrieve metadata.

In some instances, a user of the network-based data warehouse may wishto analyze different aspects of the table, such as changes ormodifications made to the table. A virtual table (or stream) can be usedfor such purposes. But because these virtual tables are tied to theunderlying table, their functionality can be limited. Moreover, thesevirtual tables can also reveal data (or the existence of such data) thatwas removed due to privacy compliance requests.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which anetwork-based data warehouse system, according to some exampleembodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIGS. 4A-B show examples of stream generation, according to some exampleembodiments.

FIG. 5 shows a system table, according to some example embodiments.

FIG. 6 shows a flow diagram for processing a privacy request, accordingto some example embodiments.

FIG. 7 illustrates an example of a stream generation using a hybridapproach, according to some example embodiments.

FIGS. 8A-8C illustrate different system table and stream generationscenarios, according to some example embodiments.

FIG. 9 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Techniques described herein can employ system tables for change datacapture determinations, such as stream reads. A system (or persistent)table may be generated storing changelog information of a primary basetable. The system table may be used to create streams of relevantinformation. In some examples, the streams may read from the systemtable for information past a retention period of the primary table whilereading from the primary table information in the retention period. Thesystem table can also be used for other applications, such as primarytable replication.

FIG. 1 illustrates an example shared data processing platform 100implementing secure messaging between deployments, in accordance withsome embodiments of the present disclosure. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents that are not germane to conveying an understanding of theinventive subject matter have been omitted from the figures. However, askilled artisan will readily recognize that various additionalfunctional components may be included as part of the shared dataprocessing platform 100 to facilitate additional functionality that isnot specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based data warehouse system 102, a cloud computing storageplatform 104 (e.g., a storage platform, an AWS® service, MicrosoftAzure®, or Google Cloud Services®), and a remote computing device 106.The network-based data warehouse system 102 is a network-based systemused for storing and accessing data (e.g., internally storing data,accessing external remotely located data) in an integrated manner, andreporting and analysis of the integrated data from the one or moredisparate sources (e.g., the cloud computing storage platform 104). Thecloud computing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based data warehouse system102. While in the embodiment illustrated in FIG. 1, a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based data warehouse system 102.The remote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views.

The network-based data warehouse system 102 comprises an accessmanagement system 110, a compute service manager 112, an executionplatform 114, and a database 116. The access management system 110enables administrative users to manage access to resources and servicesprovided by the network-based data warehouse system 102. Administrativeusers can create and manage users, roles, and groups, and usepermissions to allow or deny access to resources and services. Theaccess management system 110 can store shared data that securely managesshared access to the storage resources of the cloud computing storageplatform 104 amongst different users of the network-based data warehousesystem 102, as discussed in further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager112 also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based datawarehouse system 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-n that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-n are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-n may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-n may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based data warehouse system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based data warehousesystem 102 and the cloud computing storage platform 104. The web proxy120 handles tasks involved in accepting and processing concurrent APIcalls, including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1, data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based data warehouse system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based data warehouse system 102 toscale quickly in response to changing demands on the systems andcomponents within network-based data warehouse system 102. Thedecoupling of the computing resources from the data storage devices124-1 to 124-n supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources. Additionally, the decoupling of resources enables differentaccounts to handle creating additional compute resources to process datashared by other users without affecting the other users' systems. Forinstance, a data provider may have three compute resources and sharedata with a data consumer, and the data consumer may generate newcompute resources to execute queries against the shared data, where thenew compute resources are managed by the data consumer and do not affector interact with the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based data warehousesystem 102 is dynamic and supports regular changes to meet the currentdata processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1, the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-n in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-n. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, a request processing service 202 managesreceived data storage requests and data retrieval requests (e.g., jobsto be performed on database data). For example, the request processingservice 202 may determine the data necessary to process a received query(e.g., a data storage request or data retrieval request). The data maybe stored in a cache within the execution platform 114 or in a datastorage device in cloud computing storage platform 104. A managementconsole service 204 supports access to various systems and processes byadministrators and other system managers. Additionally, the managementconsole service 204 may receive a request to execute a job and monitorthe workload on the system. The stream share engine 225 manages changetracking on database objects, such as a data share (e.g., shared table)or shared view, according to some example embodiments, and as discussedin further detail below.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based data warehouse system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based data warehouse system 102. For example, data storagedevice 220 may represent caches in execution platform 114, storagedevices in cloud computing storage platform 104, or any other storagedevice.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, execution platform 114 includes multiplevirtual warehouses, which are elastic clusters of compute instances,such as virtual machines. In the example illustrated, the virtualwarehouses include virtual warehouse 1, virtual warehouse 2, and virtualwarehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-n shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 124-1 to124-n and, instead, can access data from any of the data storage devices124-1 to 124-n within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-n. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-n. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-n includes a cache 304-n and aprocessor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

Next, techniques for generating change capture determinations, such asstreams, are described. FIG. 4A shows an example of a stream generation,according to some example embodiments. A primary table 402 (alsoreferred to as a base table) may be provided. The primary table 402 maystore a set of data, for example customer data for a client. In someembodiments, the primary table 402 may be implemented as a view, whichallows a result of a query to be accessed as if it were a table. In someembodiments, the primary table 402 may be implemented as a set oftables.

The primary table 402 is illustrated as having two versions: T0 and T1.The versions may reflect changes (or modifications), such as datamanipulation language (DML) operations executed on the primary table402.

Data in the primary table 402 may automatically be divided into animmutable storage device referred to as a micro-partition. Amicro-partition may be an immutable storage device in a database tablethat cannot be updated in-place and must be regenerated when the datastored therein is modified. A micro-partition may be considered a batchunit where each micro-partition has contiguous units of storage. By wayof example, each micro-partition may contain between 50 MB and 500 MB ofuncompressed data (note that the actual size in storage may be smallerbecause data may be stored compressed). Groups of rows in tables may bemapped into individual micro-partitions organized in a columnar fashion.This size and structure allow for extremely granular selection of themicro-partitions to be scanned, which can be comprised of millions, oreven hundreds of millions, of micro-partitions. Metadata may beautomatically gathered about all rows stored in a micro-partition,including: the range of values for each of the columns in themicro-partition; the number of distinct values; and/or additionalproperties used for both optimization and efficient query processing. Inone embodiment, micro-partitioning may be automatically performed on alltables. For example, tables may be transparently partitioned using theordering that occurs when the data is inserted/loaded. However, itshould be appreciated that this disclosure of the micro-partition isexemplary only and should be considered non-limiting. It should beappreciated that the micro-partition may include other database storagedevices without departing from the scope of the disclosure.

A stream 404 (ST0) may be generated. A stream is a virtual table showingchange data capture (CDC) information between two points. Here, stream404 (ST0) may show the CDC information between T0 and T1 table versions.Being a virtual table, the stream does not store information itself, butinstead includes pointers to the underlying information. In thisexample, the stream 404 (ST0) includes a set of pointers to the primarytable 402. Multiple streams may be generated for different points oftime.

Over time, more changes may be executed on the primary table. FIG. 4Bshows another example of stream generation, according to some exampleembodiments. Here, the primary table 402 may now have additionalversions T2-T5 in addition to the previous two versions T0 and T1. Thestream 406 (ST1) is pointing to old table version T1, though. The datain version T1 of the primary table 402 may become stale or may exceed adata retention time of the primary table, causing data reliabilityissues. Hence, if the data is retained for an extended period of timedue to the presence of a stream (e.g., stream 404), it may causecompliance issues with privacy regulations, such as GDPR (General DataProtection Regulation). Privacy regulations, such as GDPR, can requiredeletion of personal data from data systems upon requests or othertriggering events. If data is retained for too long in old versions of atable, it may run into compliance issues with such privacy regulations.

Therefore, a system table (also referred to as a persistent tableherein) may be generated to store CDC information of the primary table.The system table may be a physical table in an optimized format, whichstores or persists information. The system table may store changeloginformation (e.g., delta information) related to the modifications ofthe primary table. In some examples, the streams may then refer andpoint to the system table instead of the primary table or in someexamples, streams may refer to both the system and primary tables, asdescribed in further detail below.

CDC information may be piped into the system table when there is achange event (e.g., DML) in the primary table. Reads from the stream maythen use the system table. Moreover, for DMLs issued on the database toensure compliance with different regulations, sensitive information canbe deleted from the primary table and the system table in an efficientmanner, as described in further detail below.

FIG. 5 shows generating a system table, according to some exampleembodiments. Here, primary table 502 includes three different versions:T1, T2, and T3. The data changes in these different versions arecaptured in a system table 504. For example, when a DML operation isexecuted on the primary table 502, corresponding change information maybe stored in the system table 502. The system table 502 may store onlychange information, thus reducing the use of storage resources and thusreducing overhead.

For example, in version T1, a row with entry “apple” for column 1 and“5” for column 2 is inserted in the primary table 502. In the systemtable 504, information regarding this row insertion is stored. Forexample, in addition to the row information (such as the column values)and the primary table version information, the system table 504 may alsostore information regarding the action (e.g., DML operation) that causedthe change and whether that action was performed as part of an update.If a row is modified, the system table 504 may record the update using acombination of operations. For example, in version T2, the previouslydiscussed row with values “apple” and “5” is modified to “9”. Thischange is recorded in the system table 504 with two entries: 1) the rowwith values “apple” and “5” as being deleted and being part of an updateoperation (Update column=TRUE), and 2) a new row with values “apple” and“9” being inserted and being part of an update operation (Updatecolumn=TRUE).

Streams, such as stream 506 (ST1), may utilize the system table 504 forreading. For example, streams may be generated based on the deltainformation stored in the system table. The streams may include pointersto the system table. Different types of streams may be supported by thesystem table. For example, a net delta stream may be supported showingthe changes between two points of time. However, if multiple changes aremade to a row during those two points of time, the intermediate changesmay not be shown in the net delta stream, only the initial and finalvalues. A full changelog stream, however, may show all changes betweentwo points of time. Both net delta and changelog streams may besupported by the system table.

FIG. 6 shows a flow diagram illustrating a method 600 for processing aprivacy request, according to some example embodiments. At operation605, a privacy request may be received. The privacy request may includea compliance request for a privacy regulation. For example, the privacyrequest may include a request to delete information for a particularcustomer. At operation 610, sensitive information related to the privacyrequest may be identified and deleted from the primary table. Forexample, a delete command for the sensitive information may be executedfor the primary table. Micro-partitions including the sensitiveinformation may be deleted from the primary table, and newmicro-partitions may be generated in the primary table excluding rowsthat contained the sensitive information.

At operation 615, the sensitive information related to the privacyrequest may be identified and deleted from the system table. To do so, adelete command may be issued for any related streams, which may in turnexplicitly delete the sensitive information from the system table. Forexample, an explicit stream delete command may be executed for thesystem table. Thus, the streams may be updated without leaving traces orindications that the sensitive information was present and then deleted.

In some example embodiments, a hybrid approach of creating streams maybe utilized. In this hybrid approach, certain streams may read from thesystem table and the primary table. For example, streams may read fromthe system table for information past a retention time of the primarytable and may read the primary table for information before theretention time. The stream may then combine or merge the informationfrom the system and primary tables to generate the stream output.

FIG. 7 illustrates an example of a stream generation using a hybridapproach, according to some example embodiments. Here, the primary table702 includes different versions, T0-T8. Versions T0-T5 are past aretention boundary of the primary table 702 and therefore may bedeleted. The retention boundary may be configurable and may be set by anadministrator.

A system table 704 may be generated to store changes in select versionsof the primary table 702. Changes in versions T1-T5 of the primary table402 may be captured in version S0 of the system table 704. In thisexample, versions T6-T8 are before the retention boundary of the primarytable 702. Consider, a stream being requested for information related toversions T1-T8 of the primary table 702, but as mentioned above,versions T1-T5 may be deleted since they are past the retention time. Astream 706 (ST1) may read from the system table 704, e.g., S0, forchange information from versions T1-T5, and the stream 706 may read fromthe primary table 702 for information from versions T6-T8. Therefore,primary table versions T0-T5 can be deleted and not retained, forexample to comply with privacy regulations, without losing relevantinformation that is now stored in the system table 702, which can beused to generate streams. This may allow reduction of the retention timeof storing prior versions of the primary table and thus reducing storageoverhead.

FIGS. 8A-8C illustrate different system table and stream generationscenarios, according to some example embodiments. FIG. 8A shows aprimary table 802 with versions T0-T5 past a retention boundary andversions T6-T8 before the retention boundary. A system table 804 is alsoillustrated. Version S0 of the system table 804 represents the changesin the primary table in versions T1-T5. However, consider that sensitiveinformation is deleted in the primary table 802 and system table 804 inresponse to a compliance request, as discussed above with reference toFIG. 6. Hence, version S1 of the system table 804 may be generated inresponse representing changes in primary table versions T1-T5 aftersensitive information has been removed corresponding to one or moredelete functions.

FIG. 8B illustrates an example of a stream generation using a hybridapproach, according to some example embodiments. Version S1 is thesystem table 804 after sensitive information has been removed, asdiscussed above with reference to FIG. 8A. Here, system table S1 may beused instead of S0 for the stream generation. Thus, a stream 806 mayread from the system table S1 for information from T1-T5 of the primarytable 802 (after data removal) past the retention boundary, and thestream 806 may also read from the primary table 802 for information fromT6-T8, which are before the retention boundary.

FIG. 8C illustrates the progression of a system table, according to someexample embodiments. Here, versions T6-T8 of the primary table 802 arenow past the retention boundary. Thus, information from T6-T8 may bemerged with the system table 804, and a new system table version S2 maybe generated accordingly.

The system table techniques described herein may find differentapplications. For example, the system table may enable more efficientgarbage collection functions. Because information regarding older datafrom the primary table may be stored in the system table (as deltainformation), older versions of the primary table may be removed morereadily. As another example, replication may be performed by using thedelta information stored in the system table. The change information inthe system table may be used to replicate the primary table or a portionthereof.

Using a system table as described herein may provide differentadvantages. For example, data retention may be set to a lower timewithout compromising access of older data for stream generation. Streamsmay also be managed more robustly. For example, streams, using systemtable techniques described herein, may be managed on a per-stream basis(e.g., with DDL) rather than all streams being affected by, for example,a DML. Sensitive information may be more protected in the streams. Forexample, query executions read from a stream may not disclose sensitiveinformation. Performance can also be improved. Streams may be generatedfaster by reading from the system table. By using optimized data formatfor the system table, storage resources may be conserved loweringstorage costs.

FIG. 9 illustrates a diagrammatic representation of a machine 900 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 900 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 916 may cause the machine 900 to execute any one ormore operations of any one or more of the methods described herein. Asanother example, the instructions 916 may cause the machine 900 toimplement portions of the data flows described herein. In this way, theinstructions 916 transform a general, non-programmed machine into aparticular machine 900 (e.g., the remote computing device 106, theaccess management system 110, the compute service manager 112, theexecution platform 114, the access management system 118, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 900 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 900 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor jointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 includes processors 910, memory 930, and input/output(I/O) components 950 configured to communicate with each other such asvia a bus 902. In an example embodiment, the processors 910 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 912 and aprocessor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 916 contemporaneously. AlthoughFIG. 9 shows multiple processors 910, the machine 900 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, all accessible to the processors 910 such as via thebus 902. The main memory 932, the static memory 934, and the storageunit 936 store the instructions 916 embodying any one or more of themethodologies or functions described herein. The instructions 916 mayalso reside, completely or partially, within the main memory 932, withinthe static memory 934, within the storage unit 936, within at least oneof the processors 910 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 900.

The I/O components 950 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 950 thatare included in a particular machine 900 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 950 mayinclude many other components that are not shown in FIG. 9. The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 950 mayinclude output components 952 and input components 954. The outputcomponents 952 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 970 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 900 may correspond to any one of the remote computing device106, the access management system 110, the compute service manager 112,the execution platform 114, the access management system 118, the Webproxy 120, and the devices 970 may include any other of these systemsand devices.

The various memories (e.g., 930, 932, 934, and/or memory of theprocessor(s) 910 and/or the storage unit 936) may store one or more setsof instructions 916 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 916, when executed by the processor(s) 910,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 980may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 980 or a portion of the network980 may include a wireless or cellular network, and the coupling 982 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 982 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 972 (e.g., a peer-to-peer coupling) to the devices 970. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended examples,along with the full range of equivalents to which such examples areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended examples, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following examples, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in an example is still deemed to fall withinthe scope of that example.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: providing a primary table storing a setof data; executing modifications to the primary table; generating asystem table storing delta information related to the modifications tothe primary table; and based on the delta information, generating changedata capture information including one or more pointers to the systemtable.

Example 2. The method of example 1, wherein the change data captureinformation includes a virtual table with the one or more pointers tothe system table.

Example 3. The method of any of examples 1-2, further comprising:defining a retention boundary for the set of data stored in the primarytable; deleting data past the retention boundary in the primary table;wherein the system table stores delta information related tomodifications executed past the retention boundary.

Example 4. The method of any of examples 1-3, wherein the change datacapture information includes one or more pointers to the system tablefor information past the retention boundary and one or more pointers tothe primary table for information before the retention boundary.

Example 5. The method of any of examples 1-4, further comprising: inresponse to a request, deleting identified information from the primarytable and deleting identified information from the system table.

Example 6. The method of any of examples 1-5, wherein deletingidentified information from the system table includes executing a deletecommand on the change data capture information.

Example 7. The method of any of examples 1-6, further comprising:replicating a portion of the primary table using the delta informationstored in the system table.

Example 8. A system comprising: one or more processors of a machine; anda memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 7.

Example 9. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 7.

What is claimed is:
 1. A method comprising: providing a primary tablestoring a set of data; executing modifications to the primary table;generating a system table storing delta information related to themodifications to the primary table; and based on the delta information,generating change data capture information including one or morepointers to the system table.
 2. The method of claim 1, wherein thechange data capture information includes a virtual table with the one ormore pointers to the system table.
 3. The method of claim 1, furthercomprising: defining a retention boundary for the set of data stored inthe primary table; deleting data past the retention boundary in theprimary table; wherein the system table stores delta information relatedto modifications executed past the retention boundary.
 4. The method ofclaim 3, wherein the change data capture information includes one ormore pointers to the system table for information past the retentionboundary and one or more pointers to the primary table for informationbefore the retention boundary.
 5. The method of claim 1, furthercomprising: in response to a request, deleting identified informationfrom the primary table and deleting identified information from thesystem table.
 6. The method of claim 5, wherein deleting identifiedinformation from the system table includes executing a delete command onthe change data capture information.
 7. The method of claim 1, furthercomprising: replicating a portion of the primary table using the deltainformation stored in the system table.
 8. A system comprising: at leastone hardware processor; and at least one memory storing instructionsthat, when executed by the at least one hardware processor, cause the atleast one hardware processor to perform operations comprising: providinga primary table storing a set of data; executing modifications to theprimary table; generating a system table storing delta informationrelated to the modifications to the primary table; and based on thedelta information, generating change data capture information includingone or more pointers to the system table.
 9. The system of claim 8,wherein the change data capture information includes a virtual tablewith the one or more pointers to the system table.
 10. The system ofclaim 8, the operations further comprising: defining a retentionboundary for the set of data stored in the primary table; deleting datapast the retention boundary in the primary table; wherein the systemtable stores delta information related to modifications executed pastthe retention boundary.
 11. The system of claim 10, wherein the changedata capture information includes one or more pointers to the systemtable for information past the retention boundary and one or morepointers to the primary table for information before the retentionboundary.
 12. The system of claim 8, the operations further comprising:in response to a request, deleting identified information from theprimary table and deleting identified information from the system table.13. The system of claim 12, wherein deleting identified information fromthe system table includes executing a delete command on the change datacapture information.
 14. The system of claim 8, the operations furthercomprising: replicating a portion of the primary table using the deltainformation stored in the system table.
 15. A machine-storage mediumembodying instructions that, when executed by a machine, cause themachine to perform operations comprising: providing a primary tablestoring a set of data; executing modifications to the primary table;generating a system table storing delta information related to themodifications to the primary table; and based on the delta information,generating change data capture information including one or morepointers to the system table.
 16. The machine-storage medium of claim15, wherein the change data capture information includes a virtual tablewith the one or more pointers to the system table.
 17. Themachine-storage medium of claim 15, further comprising: defining aretention boundary for the set of data stored in the primary table;deleting data past the retention boundary in the primary table; whereinthe system table stores delta information related to modificationsexecuted past the retention boundary.
 18. The machine-storage medium ofclaim 17, wherein the change data capture information includes one ormore pointers to the system table for information past the retentionboundary and one or more pointers to the primary table for informationbefore the retention boundary.
 19. The machine-storage medium of claim15, further comprising: in response to a request, deleting identifiedinformation from the primary table and deleting identified informationfrom the system table.
 20. The machine-storage medium of claim 19,wherein deleting identified information from the system table includesexecuting a delete command on the change data capture information. 21.The machine-storage medium of claim 15, further comprising: replicatinga portion of the primary table using the delta information stored in thesystem table.